Long-term monitoring for discrimination of different heart rhythms

ABSTRACT

A method, system, and device for detection of an arrhythmia, and discrimination between different types of arrhythmia, for example to determine whether to administer an electric shock to the heart, the device comprising a wearable monitor with electrodes that detect the electrical activity of a beating heart, attached to an embedded monitoring system having an amplifier, a microprocessor, a data storage device, and a power supply, all disposed on a substrate having large distal end portions that attach to the electrodes and a narrow intermediate portion that attaches to the monitoring system.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/125,859, filed May 22, 2008, which is a continuation-in-part of U.S.application Ser. No. 11/253,375, filed Oct. 19, 2005 (now U.S. Pat. No.7,630,756), which claims the benefit of Provisional Application No.60/620,598, filed Oct. 19, 2004, the entire disclosures of which arehereby incorporated by reference herein.

BACKGROUND

Various groups of heart rhythm disorders are known, some of which arelife-threatening and require immediate attention and treatment, such asventricular fibrillation and ventricular tachycardia, and others whichmay require treatment but not as immediately, such as atrialfibrillation. Atrial fibrillation (“AF”), for example, is a commonrhythm disturbance of the heart associated with increased risk of strokeand death. Ventricular fibrillation is much less common, but it alwaysresults in death within minutes, unless it is converted to a lessdangerous rhythm. Ventricular tachycardia is also not common, but mayresult in death if not treated promptly.

Currently, AF is diagnosed by symptoms or is discovered incidentally.Available evidence indicates that a significant portion of patients withAF do not have symptoms, and consequently may not be discovered duringroutine medical examinations unless they happen to be in atrialfibrillation at the time of the examination. However, AF may bediagnosed using medical equipment, such as rhythm monitors. Monitoringtechniques used by available rhythm monitors include monitoring theheart rhythm for a short period of time or intermittently.Unfortunately, these monitoring techniques have low sensitivity for thedetection of AF if it is not present during the short monitoring period.Conventional rhythm monitors have limited storage capacity for storingmonitoring data used to determine the extent of AF.

AF is the most common disturbance of the heart rhythm requiringtreatment. Epidemiologic data estimates that 2.2 million individualssuffer from AF in the United States. The incidence of AF increases withage. The prevalence of AF is approximately 2-3% in patients older than40 years of age and 6% in those individuals over 65 years and 9% inindividuals over 80 years old. Feinberg, W. M., et al., Prevalence, AgeDistribution, and Gender of Patients With Atrial Fibrillation, 155 Arch.Intern. Med. 469 (1995). As the U.S. population ages, AF will becomemore prevalent. It is estimated that over 5 million Americans willsuffer from AF by the year 2050. Go, A. S., et al., Prevalence ofDiagnosed Atrial Fibrillation in Adults: National Implications forRhythm Management and Stroke Prevention: the Anticoagulation and RiskFactors in Atrial Fibrillation (ATRIA) Study, 285 JAMA, 2370 (2001). AFis associated with a doubling of mortality rate of people afflicted withAF compared to people who are not, and an increased risk of stroke ofabout 5% per year. Fuster, V., et al., ACC/AHA/ESC Guidelines for theManagement of Patients With Atrial Fibrillation, 22 Eur. Heart J. 1852(October 2001).

AF can be either symptomatic or asymptomatic, and can be paroxysmal orpersistent. Symptomatic AF is a medical condition wherein symptoms arereadily detectable by experts in the field. AF is usually diagnosed whena patient reveals associated symptoms or complications, such ascongestive heart failure or stroke. AF may also be diagnosedincidentally during a routine medical evaluation. Asymptomatic AF is amedical condition wherein symptoms normally associated with AF areeither absent or are not readily detectable by experts in the field.

Paroxysmal AF comprises occasional episodes of the AF condition in thepatient. Persistent AF is a continuous existence of the condition.Patients with asymptomatic paroxysmal AF may be exposed to the risk ofdevastating consequences such as stroke, congestive heart failure, ortachycardia mediated cardiomyopathy, for years before a definitivediagnosis of AF can be made.

Current standard techniques and devices for detecting AF include aresting electrocardiogram, which records about 15 seconds of cardiacactivity, a Holter monitor, which records 24-48 hours of cardiacactivity during routine daily activities, and an event monitor, whichonly records cardiac activity when the patient activates the monitorbecause the patient has detected symptoms associated with AF. Thesediagnostic methods and tools have significant limitations in diagnosingAF and assessing the efficacy of treatment because of the limitedrecording time windows of these methods and tools. The prevalence ofasymptomatic AF is difficult to assess, but is clearly underrepresentedin the figures quoted above.

Pharmacologic treatment of AF may convert patients with symptomatic AFinto patients with asymptomatic AF. In a retrospective study of fourstudies comparing Azimilide drug to placebo where, in the absence ofsymptoms, routine trans-telephonic electrocardiograms were recorded for30 seconds every two weeks, asymptomatic AF was present in 17% of thepatients. Page, R. L., et al., Asymptomatic or “Silent” AtrialFibrillation: Frequency in Untreated Patients and Patients ReceivingAzimilide, 107 Circulation 1141 (2003).

In another study of 110 patients with permanently implanted pacemakerswho had a history of AF, the condition was diagnosed in 46% of thepatients using electrocardiogram (“EKG”) recording and in 88% of thepatients using stored electrograms recorded by the implanted pacemaker.Israel, C. W., et al., Long-Term Risk of Recurrent Atrial Fibrillationas Documented by an Implantable Monitoring Device, 43 J. Am. Coll.Cardiol. 47 (2004).

Review of data stored in implanted devices, such as pacemakers, revealedthat 38% of AF recurrences lasting greater than 48 hours were completelyasymptomatic. Finally, using data obtained from ambulatory monitors usedon patients with paroxysmal AF over a 24-hour period, studies show ahigh frequency of occurrence of asymptomatic AF among patients treatedwith propranolol or propafenone drugs. Wolk, R., et al., The Incidenceof Asymptomatic Paroxysmal Atrial Fibrillation in Patients Treated WithPropranolol or Propafenone, 54 Int. J. Cardiol. 207 (1996). In theabove-mentioned study, 22% of the patients on propranolol and 27% of thepatients on propafenone were diagnosed with AF without symptoms.

There is also evidence that previously undetected AF is associated withstroke. About 4% of patients with stroke admitted to a medical facilityalso had newly diagnosed AF which was thought to be a precipitatingcause of the stroke. Lin, H. J., et al., Newly Diagnosed AtrialFibrillation and Acute Stroke, The Framingham Study, 26 Stroke 1527(1995).

Under-detection and under-recognition of AF in patients may havesignificant clinical consequences. A first consequence includes clinicalexposure of patients to a significant risk of cardioembolic strokebefore detection of the arrhythmia and initiation of appropriate strokeprevention measures.

A second consequence includes difficulty of assessment of the efficacyof rhythm control intervention. Physicians caring for such patients mayerroneously conclude that AF is no longer present and inappropriatelydiscontinue anticoagulation treatments which may lead to a devastatingcardioembolic stroke. Consequently, once diagnosed with AF, manypatients may be committed to life-long anticoagulation by the physicianto avoid the latter issues.

A third consequence includes overestimation of successful maintenance ofsinus rhythm. Clinical studies evaluating the efficacy of various rhythmcontrol strategies may overestimate the successful maintenance of sinusrhythm as many of these studies report symptomatic AF as an endpoint. Anaccurate long term monitoring device would enhance the diagnostic yieldof capturing asymptomatic paroxysmal atrial fibrillation, potentiallyallowing the safe withdrawal of anticoagulation treatments in patientstreated successfully with anti-arrhythmic agents, identifying thepatients at risk who are currently not diagnosed as having AF, andproviding a more precise measure of the efficacy of pharmacologic andnon-pharmacologic rhythm control strategies.

Detection of AF, automatically or manually, based on statistical data,requires the use of thresholds defined with respect to sensitivity andspecificity. The thresholds used define the point beyond which a set ofdata indicate existence of AF. Sensitivity and specificity are definedas follows. In a dichotomous experiment, a given event, e, falls intoone of two sets, such as a set of positive events, P, and a set ofnegative events, N. The set P includes events p and the set N includesevents n.

A detection test may be performed to determine that the given event ebelongs to the set P or to the set N in a dichotomous experiment.Sensitivity is a measure of how well the detection test can correctlyidentify the given event e of the set P as belonging to the set P. Suchevents e that are correctly identified as belonging to the set P areknown as true positives (“TP”). Such events e that are misidentified asbelonging to the set N are known as false negatives (“FN”).

Sensitivity is defined as the ratio of the number of true positiveevents detected correctly by the test to the total number of actualpositive events p. The total number of actual positive events is equalto the sum of the TP and FN. That is, sensitivity=TP/(TP+FN). A lowsensitivity detection test will misidentify more positive events asbelonging to the set N than a high sensitivity detection test.

Specificity is the dual of sensitivity and is a measure of how well thedetection test can correctly identify the given event e of the set N asbelonging to the set N. Such events e that are correctly identified asbelonging to the set N are known as true negatives (“TN”). Such events ethat are misidentified as belonging to the set P are known as falsepositives (“FP”). Specificity is defined as the ratio of the number oftrue negative events detected correctly by the test to the total numberof actual negative events n. The total number of actual negative eventsis equal to the sum of the TN and FP. That is, specificity=TN/(TN+FP). Alow specificity detection test will misidentify more negative events asbelonging to the set P than a high specificity detection test.

A number of techniques have been used for the automated detection of AFfrom digitized electrocardiograms. One of the techniques used includesthe use of intracardiac recordings obtained from implanted devicesshowing a sensitivity of close to 100% and a specificity of greater than99%. Swerdlow, C. D., et al., Detection of Atrial Fibrillation andFlutter by a Dual-Chamber Implantable Cardioverter-Defibrillator, 101Circulation 878 (2000).

A method for analysis of the surface monitor leads using a wavelettransform achieved a sensitivity of 96% and specificity of 93% inrecordings from patients with paroxysmal atrial fibrillation. Duverney,D. et al., High Accuracy of Automatic Detection of Atrial FibrillationUsing Wavelet Transform of Heart Rate Intervals, 25(4) Pacing andClinical Electrophysiology 457 (2002). At least one group has proposedusing wavelets for implantable/wearable monitoring devices. Ang, N. H.,Real-Time Electrocardiogram (ECG) Signal Processing for AtrialFibrillation (AF) Detection, Modeling Seminar—Archive (2003).

A prominent characteristic of AF is heart rate variability. There havebeen attempts to use the variability of heart interbeat (“RR”) intervalsdirectly to identify AF, resulting in a sensitivity of 94% andspecificity of 97% using a threshold based on the Kolmogorov-Smirnovtest. Tateno, K. and Glass, L., Automatic Detection of AtrialFibrillation Using the Coefficient of Variation and Density Histogramsof RR and ΔRR Intervals, 39(6) Med. Biol. Eng. Comput. 664 (2001). TheKolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used todecide if a statistical sample belongs to a population with a specificprobability distribution.

Long-term monitoring of cardiac activity is desirable for timelydetection of AF, but the storage requirements can be prohibitive. Todigitize a single channel EKG at 100 samples per second and 10-bitresolution, which constitute near minimum requirements for a highquality signal, for 90 days of continuous recording requires 927megabytes of storage. Although providing this amount of storage ispossible, it is also costly. Advances in electronics allow the design ofportable devices that can pre-process and classify the signals to avoidstorage of normal rhythms and save the storage capacity for recording ofabnormal rhythms indicating existence of atrial fibrillation.

Selective storage of signals that potentially indicate AF as opposed tonormal heart rhythm effectively increases the storage capacity andprolongs the recording period. At least two such devices exist in themarket. One such device has been developed by Instromedix (San Diego,Calif.), and is available in two versions. Each version can monitor theheart rhythm for up to 30 days, capturing a total of 10 minutes ofpotentially abnormal EKG. The device weighs about 4 ounces. The otherdevice is based on satellite telephone technology, and transmits thesuspect rhythms to a monitoring facility. For such a device, accuratealgorithms are also important, since high sensitivity will result in ahigh probability of detection, and high specificity will avoidtransmission and review of normal rhythms.

Recently, another device was announced with a detection rate of 90% anda monitoring storage capacity equivalent to 60 minutes of recorded data.A device for home use which does “momentary” analysis of theelectrocardiogram as the patient grasps handles on the device daily isdisclosed in U.S. Pat. No. 6,701,183, issued to Baker et al. on Mar. 2,2004, entitled Long Term Atrial Fibrillation Monitor.

A device is desired for the long-term monitoring of AF that isinexpensive, non-invasive, highly accurate, and convenient for thepatient. These requirements at least indicate that the monitoring deviceshould be light and small. As such, a device is desired with low powerrequirements and with a significant amount of storage. The storagecapacity may possibly be extended by using an algorithm for theelimination of EKG data that indicate very low-probability of AF. Thisalgorithm should be small in size and simple in operation to reduceprocessing power needs and electrical power requirements.

The existing algorithms based on wavelets appear to be overly complexfor this type of application, requiring a significant amount ofprocessing and electrical power as well as storage capacity.

As should be apparent to one skilled in the art, there are situationswhere the accurate detection of AF would be desirable in an implantabledevice as well. There are implantable devices that are intended solelyfor diagnosis of rhythm disturbances, and devices implanted for therapywhich have additional diagnostic functions. These devices would alsobenefit from accurate, low computational complexity detection of AF. Itshould also be apparent that devices intended to treat AF, either byelectric shocks delivered to the heart, or medications administered tocontrol atrial fibrillation, such as propafenone, amiodarone orbeta-blockers, or to convert AF, such as ibutilide, would benefit fromaccurate, low-computational cost complexity detection of AF.

For response to and treatment of ventricular tachycardia and ventricularfibrillation in real-time, often an implantable cardiac defibrillator(“ICD”) is surgically implanted in the patient and coupled with theheart to monitor heart rhythm and detect these life-threatening rhythmdisturbances. The ICD typically includes logic components implemented insoftware/firmware and/or hardware for detecting arrhythmia. Oncelife-threatening arrhythmia is detected, the ICD logic component may,based on a discrimination algorithm, determine that some action, such asadministering an electric shock (defibrillation), must be taken to treatthe arrhythmia.

However, this determination can be erroneous. In some ICDs suchinappropriate shocks can occur in 15% of all patients within a 46-monthfollow-up. Alter, P., et al., Complications of Implantable CardioverterDefibrillator Therapy in 440 Consecutive Patients, 28(9) Pacing andClinical Electrophysiology, 926 (2005). Certain sub-populations may havea higher rate of inappropriate shocks, for example, this can occur in asmany as 38% of younger patients. Costa, R., et al., Incidence of Shockand Quality of Life in Young Patients with ImplantableCardioverter-Defibrillator, 88(3) Arq. Bras. Cardiol. 258 (2007). Acommon cause of inappropriate shock in these patients is AF, althoughvirtually any supraventricular tachycardia can cause an inappropriateshock.

When not needed, an electric shock causes extreme discomfort and/or painto the patient and may be potentially dangerous. Accordingly, a moreaccurate discrimination algorithm is needed to discriminate betweencases where an electric shock is needed and cases where an electricshock is not needed. One approach that has been used is based on theheart rate, or beat-to-beat intervals, as in a recent study by Mletzko.Mletzko, R., et al., Enhanced Specificity of a Dual Chamber ICDArrhythmia Detection Algorithm by Rate Stability Criteria, 27(8) Pacingand Clinical Electrophysiology 1113 (2004).

Another approach uses the morphology of the electrocardiographiccomplexes to help discriminate, with a representative recent methodbeing the use of wavelet-transforms. Klein, G. J., et al., Improving SVTDiscrimination in Single-Chamber ICDs: A New ElectrogramMorphology-Based Algorithm, 17(12) J. Cardiovascular Electrophysiology1310 (2006). In spite of these methods, inappropriate shocks continue tobe a problem in such devices.

An additional constraint is that, since the devices are battery poweredand implanted, power consumption is a major consideration. Cebrian, A.,et al., Implantable Cardioverter Defibrillator Algorithms: Status Reviewin Terms of Computational Cost, 52(1) Biomed. Tech. (Berl) 25 (2007).

The power consumption is related to the complexity of the algorithm, andthe hardware required. The ideal algorithm would not require specializedhardware, and would have a low computational complexity, so that powerconsumption would be low.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

A method and a system for long-term monitoring and detection of anarrhythmia, discrimination between different types of arrhythmia, anddelivering an electric shock or other therapy upon detection of anappropriate type of arrhythmia, the method comprising determining anumber of heart beat intervals; determining an instantaneous heart ratefor each of the heart beat intervals; removing the trend of theinstantaneous heart rates; determining the variability of the de-trendedinstantaneous heart rates compared to a mean of the number ofinstantaneous heart rates; determining a non-linear value thatrepresents the variability of the instantaneous heart rates; using alinear combination of the mean heart rate and the non-linear value tocalculate a result; and using the result to discriminate betweendifferent types of arrhythmia by comparing the result with apredetermined threshold.

In one illustrative embodiment, an implantable cardiac device fordiscriminating between different types of arrhythmia is disclosed, thedevice including a portable power source; electrodes for collectingheart rhythm data from a patient and delivering an electric pulse to theheart upon detection of an appropriate type of arrhythmia; a monitoringcircuit coupled to the power source and the electrode, wherein themonitoring circuit analyzes a segment of the collected heart rhythm datato detect an arrhythmia, discriminate between different types ofarrhythmia, and deliver an electric pulse to the heart upon adetermination that an appropriate type of arrhythmia has been detected;and a memory coupled to the monitoring circuit for storing the heartrhythm data.

A wearable arrhythmia monitoring and detection device is disclosed thatincludes an elongate substrate with enlarged ends connected by anarrower intermediate section. The enlarged ends incorporate electrodes,and the narrower intermediate portion includes an integrated monitoringsystem. The monitoring system includes an amplifier that receiveselectrocardiography signals from the electrodes, a microprocessor thatreceives amplified signals from the amplifier, a data storage devicethat stores at least a portion of the data received from themicroprocessor, and a power supply. The wearable arrhythmia monitoringand detection device is configured to detect a cardiac arrhythmia, forexample atrial fibrillation.

In an embodiment, only electrocardiography data associated with adetected cardiac arrhythmia is stored on the data storage device, andthe data may be compressed prior to such storage.

In an embodiment, the monitoring system is configured to perform one ormore of the following functions: (i) determine the duration of heartbeat intervals from the electrocardiography data for a selected analysissegment comprising a plurality of heart beat intervals; (ii) calculatean instantaneous heart rate for each of the heart beat intervals in theanalysis segment; (iii) calculate a test result using the calculatedinstantaneous heart rates; (iv) compare the test result with apredetermined threshold to discriminate between different types ofcardiac arrhythmia; (v) calculate a mean instantaneous heart rate forthe analysis segment; (vi) calculate a deviation from the meaninstantaneous heart rate for each heart beat interval in the analysissegment; and (vii) determine a median deviation from the meaninstantaneous heart rate in the analysis segment; and use the meaninstantaneous heart rate and the median deviation to calculate the testresult.

In an embodiment, the monitoring system is configured to calculate atest value as a linear combination of the mean instantaneous heart rateand the median deviation. In an embodiment the monitoring system isconfigured to use an analysis segment comprising between five andnineteen heart beat intervals.

In an embodiment the integrated monitoring system further includes awave detection device, such as a QRS detector.

DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same become betterunderstood by reference to the following detailed description, whentaken in conjunction with the accompanying drawings, wherein:

FIG. 1A is a pictorial diagram of an illustrative portable atrialfibrillation monitoring and detection device as applied to a patient;

FIG. 1B is a pictorial diagram of another illustrative portable atrialfibrillation monitoring and detection device as applied to a patient;

FIG. 1C is a pictorial diagram of an illustrative implantable monitoringand therapeutic device as applied to a patient;

FIG. 1D is a pictorial diagram of the illustrative implantabletherapeutic device illustrated in FIG. 1C;

FIG. 2A is a pictorial diagram of the portable atrial fibrillationmonitoring and detection device shown in FIG. 1A;

FIG. 2B is a pictorial diagram of the portable atrial fibrillationmonitoring and detection device shown in FIG. 1B;

FIG. 3A is a pictorial diagram of an illustrative operating environmentfor downloading and uploading data from/to a portable atrialfibrillation monitoring and detection device;

FIG. 3B is a pictorial diagram of another illustrative operatingenvironment for downloading and uploading data from/to a portable atrialfibrillation monitoring and detection device;

FIG. 3C is a pictorial diagram of another illustrative operatingenvironment for downloading and uploading data from/to a portable atrialfibrillation monitoring and detection device;

FIG. 3D is a pictorial diagram of another illustrative operatingenvironment for downloading and uploading data from/to a portable atrialfibrillation monitoring and detection device;

FIG. 4A is a block diagram of an illustrative embodiment of a circuitfor monitoring and detection of atrial fibrillation, including ahardware-based QRS signal detector;

FIG. 4B is a block diagram of another illustrative embodiment of acircuit for monitoring and detection of atrial fibrillation;

FIG. 5 is a pictorial diagram of a signal representing a heart rhythm;

FIG. 6 is a pictorial diagram of a signal representing a QRS portion ofthe heart rhythm;

FIG. 7 shows illustrative box plot graphs of values obtained for atrialfibrillation and for a normal heart rhythm;

FIG. 8 is an illustrative graph of values for Sensitivity versus(1—Specificity);

FIG. 9 is an illustrative graph showing a number of RR intervals versusarea under a Receiver Operator Curve (“ROC”) for the atrial fibrillationdetection algorithm;

FIG. 10 is an illustrative time line of RR intervals, and showing threesegments of seven RR intervals;

FIG. 11A is a flow diagram of an illustrative atrial fibrillationdetection method in accordance with the present invention;

FIG. 11B is a flow diagram of an illustrative arrhythmia discriminationmethod in accordance with the present invention; and

FIG. 12 is a flow diagram of an illustrative QRS detection method.

DETAILED DESCRIPTION

A system and a method for detecting cardiac fibrillation and/or fordiscriminating between classes of arrhythmia is disclosed. In oneembodiment, a method and apparatus for long-term monitoring anddetection of atrial fibrillation is disclosed. In another embodiment, amodified version of the method and a device are disclosed fordiscriminating heart signal data to detect and accurately identify andcorrectly distinguish life-threatening cardiac rhythms that require anelectric shock (defibrillation) from other rhythms that do not require ashock. While the system and method are ideally suited for detectingatrial fibrillation, or discrimination of life-threatening rhythms fromother rhythms, the system and method may also find use in otherenvironments. Furthermore, while the system and method are described inportable configurations and environments, the system and method may alsofind use in fixed and static environments. Thus, it is to be understoodthat the present invention should not be construed as limited inapplication to the illustrative embodiments described herein, and suchillustrative embodiments should not be construed as limiting.

FIG. 1A is a pictorial diagram showing an illustrative operatingenvironment for a portable atrial fibrillation monitoring and detectiondevice (“portable monitoring device”). This illustrative operatingenvironment includes a light-weight, small, portable monitoring device106 which can be used by a patient 108 daily or continuously for severalmonths. The portable monitoring device 106 may be carried on a belt 110or other harness. The portable monitoring device 106 shown includes twoelectrodes 102 and 104 which are attached to the body of the patient 108for recording cardiac activity. The electrodes 102 and 104 may be fixedto the portable monitoring device 106 or may be detachably connected tothe portable monitoring device 106. The electrodes 102 and 104 may beattached to the body of the patient 108 using various means, includingadhesive surfaces, rubber bands, or various kinds of straps andharnesses for holding the electrodes 102 and 104 in place. Theelectrodes 102 and 104 may also be attached to the body of the patient108 by being attached to or implanted in the garment of the patient 108.The portable monitoring device 106 continuously collects data related tocardiac activity from the electrodes 102 and 104, and stores some or allof the collected data in the internal storage component of the portablemonitoring device 106.

FIG. 1B is a pictorial diagram showing another illustrative operatingenvironment for a portable atrial fibrillation monitoring and detectiondevice wherein the portable monitoring device and attached electrodesare integrated into a unitary monitoring device 112. In thisillustrative operating environment, the integrated monitoring device 112attaches directly to the body of the patient 108 without the need forthe belt 110 or other harness for supporting the device. The integratedmonitoring device 112 is sufficiently thin and light-weight to securelyattach to the body of the patient 108, for example, by means of adhesivesurfaces, and to be worn under normal clothes without undue burden. Thepatient 108 may wear the integrated monitoring device 112 for extendedperiods of time, removing and re-attaching the integrated monitoringdevice 112 as necessary for other activities, while noting the times ofinterruption of the recording operation.

FIG. 1C is a pictorial diagram showing an implantable monitoring,detection, and intervention device 116 implanted into the patient 108.In this example embodiment, the device 116 comprises an implantablecardiac defibrillator 116 that is surgically implanted in the patient108 and coupled with the heart 114 through one or more leads 115 (oneshown). The device 116 monitors heart rhythm to detect arrhythmia,discriminates any detected arrhythmia to identify conditions for whichintervention is indicated, and administers the desired intervention. Forother arrhythmias not requiring immediate intervention, the device maystore the information characterizing the event for transmission ordownload, either immediately or at a later time.

In certain applications when life-threatening heart arrhythmia isdetected (e.g., ventricular fibrillation) therapeutic or remedial actionor intervention is needed immediately, such as providing appropriateelectrical shock to the heart to stabilize or regulate the heart beat.For example, a patient having a life-threatening heart condition mayelect to have an ICD implanted in to continuously monitor heart rhythmand intervene when indicated, for example by providing an appropriateelectrical shock to the patient's heart. It is also contemplated that animplantable device 116 may alternatively or additionally be operable todispense suitable medications in response to a detected arrhythmia.

FIG. 1D shows a pictorial diagram of the single-lead ICD 116 shown inFIG. 1C, wherein the right ventricular lead 115 extends into thepatient's heart 114. Although a single-lead ICD 116 is shown, it will beapparent that the present invention may be implemented with a multi-leaddefibrillator, e.g., a defibrillator having a coronary sinus lead,atrial lead and ventricular lead, as are known in the art. The ICD 116is surgically implanted in the patient and coupled with the heart 114 tomonitor heart rhythm, and to detect and treat arrhythmia whenintervention is indicated. In one illustrative embodiment, the ICD 116includes logic components implemented as embedded software/firmwareand/or hardware for detecting arrhythmia, as discussed below. Theembedded software may be updateable via wireless communicationcomponents 124 incorporated into the ICD 116.

Similarly, data collected and/or generated by the ICD 116 may be storedin a data store 126, which is externally readable from the ICD 116 viathe wireless communication components 124, for further analysis. If anarrhythmia is detected, the ICD 116 logic component determines whether ashock must be administered to treat the arrhythmia. The ICD 116 mayinclude other processing components such as amicroprocessor/micro-controller for executing software/firmware andcontrolling other functions of the ICD 116, a power source, such as abattery, and other hardware and/or software components known in the artand commonly used for these types of devices.

With continued reference to FIG. 1D, the ICD 116 is coupled with heart114 via one or more leads and electrodes 115 to detect arrhythmia andadminister electric shock when needed. In this embodiment, the rightventricular lead 115 is used to detect the electrical activity of theheart 114 and to deliver therapeutic electric pulses or shock to theheart 114 in response to the detection of a type of arrhythmia for whichelectric shock is an appropriate treatment. The determination of thenecessity of electric shock and the delivery of such electric shock isperformed under the control of the logic components embedded in ICD 116.

An arrhythmia discrimination method is used to determine if a detectedarrhythmia is of a type that requires electric shock treatment. If anelectric shock is indicated for the type of arrhythmia detected, thenthe arrhythmia discrimination algorithm will signal the delivery of anelectric shock to the heart 114. Otherwise, no electric shock isdelivered. The currently preferred discrimination method is describedmore fully below with respect to FIG. 11B.

FIG. 2A shows an illustrative embodiment of a portable atrialfibrillation monitoring and detection device 202. The deviceconfiguration 200 shown includes the portable monitoring device 202,electrodes 214, 216 and leads 212 coupling the electrodes 214, 216 tothe portable monitoring device 202. In one illustrative embodiment, theelectrodes 214, 216 are fixed to the portable monitoring device 202. Inanother illustrative embodiment, the electrodes 214, 216 are detachablyconnected to the portable monitoring device 202. In one illustrativeembodiment, the portable monitoring device 202 comprises at least onedisplay 210, such as a liquid crystal display (“LCD”) panel, to showvarious information about the data and the status of the device.

In another illustrative embodiment, the portable monitoring device 202comprises an indicator 208, such as a light emitting diode (“LED”), tocommunicate information to the user of the device, for example, byblinking or by using different colors of light. The portable monitoringdevice 202 further comprises at least one input means 204, such as abutton, to control the settings and the behavior of the portablemonitoring device 202. In another illustrative embodiment, the portablemonitoring device 202 comprises two such input means 204 and 206, one ofwhich may be used by the patient and the other one by a technicianduring data retrieval or repairs. The portable monitoring device 202further comprises at least one communication port 218 which is used todownload and upload information to and from the portable monitoringdevice 202, respectively. The information communicated through port 218includes data collected by the device, device status, deviceconfiguration settings, and device software program updates.

In one illustrative embodiment, the portable monitoring device 202further comprises internal circuitry (not shown in this figure) thatinclude programmable devices, such as a microcontroller or amicroprocessor, and internal software programs that are executed by themicrocontrollers or microprocessors to cause the portable monitoringdevice 202 to collect data and perform other functions as discussedbelow. In another illustrative embodiment, the portable monitoringdevice 202 comprises internal circuitry (not shown in this figure) thatinclude devices that operate independent of software for some aspects ofthe operation of the portable monitoring device 202, for examplecounting wave peaks and wave pattern detection. In one illustrativeembodiment, the portable monitoring device has a weight of less than oneounce and a volume of less than ten cubic centimeters. It will beappreciated by those skilled in the art that the shape and dimensions ofthe portable monitoring device 202 shown in FIG. 2A are for the purposeof illustration and discussion and should not be construed as a limit onthe invention.

The portable monitoring device 202 used for detection of arrhythmia andthe ICD 116 used for detection, discrimination of different types ofarrhythmia, and intervention, may be used as part of one treatmentregimen or separately for different medical applications. These devicesand the respective algorithms used in each, as more fully describedbelow with respect to FIGS. 11A and 11B, respectively, share certainfeatures and are different in other important ways in terms of design,application, and results they provide.

In one illustrative embodiment the portable monitoring device 202 andthe ICD 116 are integrated as functionally distinct units into oneintegrated implantable device (not shown in the figures) for detectionof and discrimination between different types of arrhythmia. In such anintegrated device each of the functional detection and discriminationunits performs its own function as described below. In such anintegrated implantable device, the functional detection anddiscrimination components may share common functions and features, suchas collection of heart rhythm data, computation of mean and median, andother processing tasks and/or hardware/software components that arecommon to both functional units. In another illustrative embodiment, theportable monitoring device 202 and the ICD 116 are implemented asseparate devices that are used in conjunction with each other orseparately, as noted above.

FIG. 2B is a pictorial diagram showing another illustrative embodimentof an integrated portable atrial fibrillation monitoring and detectiondevice 250. In this illustrative embodiment, the integrated monitoringdevice 250 includes a processing component 256 built into the body ofintegrated monitoring device 250, and at least two integrated electrodes252 and 254. In another illustrative embodiment, the integratedmonitoring device 250 includes more than two integrated electrodes,taking the form of a star with multiple electrode arms extending fromthe body of the integrated monitoring device 250. In this illustrativeembodiment, the integrated monitoring device 250 is sufficiently thinand light-weight to securely attach to the body of the patient, forexample, by means of adhesive surfaces, and to be worn under normalclothes without undue burden. In one illustrative embodiment, theintegrated monitoring device 250 further comprises internal circuitry(not shown in this figure) that include programmable devices, such as amicrocontroller or a microprocessor, and internal software programs thatare executed by the microcontrollers or microprocessors to cause theintegrated monitoring device 250 to collect data and perform otherfunctions as discussed below. In another illustrative embodiment, theintegrated monitoring device 250 comprises internal circuitry (not shownin this figure) that include devices that operate independent ofsoftware for some aspects of the operation of the integrated monitoringdevice 250, for example counting wave peaks and wave pattern detection.It will be appreciated by those skilled in the art that the shape anddimensions of the integrated monitoring device 250 shown in FIG. 2B arefor the purpose of illustration and discussion and should not beconstrued as a limit on the invention.

FIGS. 3A-3D show several illustrative operating environments fordownloading and uploading data from/to a portable atrial fibrillationmonitoring and detection device. The portable monitoring device 302shown in the above-mentioned drawings represents all embodiments of suchmonitoring device, including the portable monitoring device 202 and theintegrated monitoring device 250 discussed above.

FIG. 3A is a pictorial diagram showing an illustrative operatingenvironment 300 for downloading and uploading data from/to a portableatrial fibrillation monitoring and detection device 302. In theoperating environment 300, the portable monitoring device 302communicates with a computer or other data processing equipment at amedical facility or monitoring center (not shown in this figure) wherethe collected data are used for processing and analysis and maintenanceand setup operations are performed on the portable monitoring device302. In one illustrative embodiment, the portable monitoring device 302includes a wireless module which communicates data to a computer orother data processing equipment using electromagnetic waves 306. In oneillustrative embodiment, the wireless module of the portable monitoringdevice 302 includes a Bluetooth wireless interface. In anotherillustrative embodiment, the wireless module of the portable monitoringdevice 302 includes a ZigBee® wireless interface. As would be clear toone skilled in the art, there are a number of wireless systems orstandards that may alternatively be utilized. In another illustrativeembodiment, the portable monitoring device 302 uses a wired interface304, for example, RS232 serial bus, universal serial bus (“USB”), orFirewire, to communicate data. The data communicated by the portablemonitoring device 302 includes data collected by the device, devicestatus, device configuration settings, and other similar information.The communication of data may be from or to the portable monitoringdevice 302. The portable monitoring device 302 may also receiveinformation from outside, for example, from a technician or a computer,using the wireless module or the wired interface 304. Such informationmay include configuration settings and software program updates.

FIG. 3B is a pictorial diagram showing another illustrative operatingenvironment 310 for downloading and uploading data from/to a portableatrial fibrillation monitoring and detection device 302. In theoperating environment 310, the portable monitoring device 302communicates with a computer or other data processing equipment at amedical facility or monitoring center (not shown in this figure) wherethe collected data are used for processing and analysis and maintenanceand setup operations are performed on the portable monitoring device302. In one illustrative embodiment, the portable monitoring device 302includes a removable memory module 312 which may be removed by atechnician at a medical facility or monitoring center for retrieval ofdata collected by the portable monitoring device 302. In anotherillustrative embodiment, the memory module 312 is removed by the patientand mailed to the medical facility or monitoring center. Many types ofmemory devices are available that may be used as embodiments for thememory module 312. For example, in one embodiment, the memory module 312includes a secure digital (“SD”) memory card. In another illustrativeembodiment, the memory module 312 includes a Personal Computer MemoryCard International Association (“PCMCIA”) flash type memory card. Yet inanother illustrative embodiment, the memory module 312 includes acompact flash card. Still in another illustrative embodiment, the memorymodule 312 includes a Multimedia card (“MMC”). Still in anotherillustrative embodiment, the memory module 312 includes a memory stick.The information contained in the memory module 312 generally includesthe data collected by the portable monitoring device 302, but mayoptionally include other information, such as device status, deviceconfiguration settings, and device software program updates. Newsoftware program updates for the portable monitoring device 302 may beincluded in the memory module 312.

FIG. 3C is a pictorial diagram showing another illustrative operatingenvironment 320 for downloading and uploading data from/to a portableatrial fibrillation monitoring and detection device 302. In theoperating environment 320, the portable monitoring device 302communicates with a base station 322 at the patient's home or otherremote location away from a medical facility or monitoring center wherethe collected data is processed and analyzed. The base station 322 mayalso be used for uploading software program updates, configurationsettings, and other information to the portable monitoring device 302.In one illustrative embodiment, the portable monitoring device 302includes a wireless module which communicates with base station 322using electromagnetic waves 306. In one illustrative embodiment, thewireless module of the portable monitoring device 302 includes aBluetooth® wireless interface. In another illustrative embodiment, thewireless module of the portable monitoring device 302 includes a ZigBee®wireless interface. In one illustrative embodiment, the base station 322is connected to the Internet 324 using various methods of connection,such as a dialup connection, acoustic coupler, wired Ethernetconnection, cell phone or wireless Internet connection, such as Wi-Fi®.In another illustrative embodiment, the base station 322 is connected tothe medical facility or monitoring center using a direct connection,such as a dedicated network connection, and direct dialup to a serverused by the medical facility or monitoring center. The data from theportable monitoring device 302 is transferred to a medical facility ormonitoring center where the data is processed and analyzed using thebase station 322 and the Internet 324 or other connections, as discussedabove.

FIG. 3D is a pictorial diagram showing another illustrative operatingenvironment 330 for downloading and uploading data from/to a portableatrial fibrillation monitoring and detection device 302. In oneembodiment, the portable monitoring device 302 is seated in a basestation 332 whereby an electrical data interface is used to establish aconnection between the portable monitoring device 302 and the basestation 332. In one illustrative embodiment, the base station 322 isconnected to the Internet 324 using various methods of connection, suchas a dialup connection, an acoustic coupler, a wired Ethernetconnection, and/or a wireless local area network. In anotherillustrative embodiment, the base station 322 is connected to themedical facility or monitoring center using a direct connection such asa dedicated network connection and direct dialup to a server used by themedical facility or monitoring center. The data from the portablemonitoring device 302 is transferred to a medical facility or monitoringcenter where the data is processed and analyzed using the base station332 and the Internet 324 or other connections, as discussed above.

FIG. 4A is a block diagram showing an illustrative embodiment of acircuit 400 for monitoring and detection of atrial fibrillation,including a hardware-based QRS complex signal detector 416 (“QRSdetector”). In one illustrative embodiment, the monitoring circuit 400includes a preamplifier 402 for amplifying the analogelectrocardiographic signals detected by electrodes and presented atinput terminals 418 and 420. The output of the preamplifier 402 is inputto microprocessor 408 and QRS detector 416. In one embodiment, the QRSdetector 416 comprises a peak detector. In another embodiment, the QRSdetector 416 comprises a peak detector with hysteresis. In yet anotherembodiment, the QRS detector 416 comprises a signal correlator thatmatches an input signal to a reference signal (not shown in thisfigure).

The microprocessor 408 is coupled with a data interface 412 via a databus 414. The microprocessor 408 is further coupled with a data storagecomponent 410 used for storing data collected by the microprocessor 408from the preamplifier 402 and for storing software programs executed bythe microprocessor 408. A power supply 404 supplies power to allelectronic components using power bus 406. In one illustrativeembodiment, the power supply 404 comprises a battery. In oneillustrative embodiment, the electronic components used in themonitoring circuit 400 are off-the-shelf components. In anotherillustrative embodiment, the electronic components compriseapplication-specific integrated circuits or other custom-madeelectronics. In one embodiment, the microprocessor is a high-integrationcomponent including an analog-to-digital converter and memory and datainterfaces. The microprocessor and other electronic components areselected to have low power consumption. Low power consumption ofelectronic components enables the monitoring circuit 400 to operatecontinuously for extended periods of time on a limited power source,such as a battery. It will be appreciated by those skilled in the artthat other electronic components not shown in FIG. 4A, such as LCDdisplay, buttons, LED, and the like, may be coupled to the circuit 400.

The operation of the monitoring circuit 400 includes thepre-amplification of the analog electrocardiographic signals at inputterminals 418 and 420 by the preamplifier 402. The amplified analogelectrocardiographic signal at the output of preamplifier 402 istransmitted to the microprocessor 408 and QRS detector 416. Themicroprocessor 408 converts the analog electrocardiographic signal fromthe output of the preamplifier 402 to a digital electrocardiographicsignal suitable for manipulation by a software program running on themicroprocessor 408. In one embodiment, the software program running onthe microprocessor 408 is stored in a designated section of the datastorage component 410. In another embodiment, the software programrunning on the microprocessor 408 may be stored in a different memorycomponent (not shown in this figure) that is distinct from the datastorage component 410. Yet in another embodiment, the software programrunning on the microprocessor 408 may be stored in a memory componentintegrated with the microprocessor 408 on the same electronic chip.

The microprocessor 408 receives an output signal of the QRS detector 416when the QRS detector 416 detects a QRS complex signal whichperiodically appears as a segment of the electrocardiographic signal.The software program running on the microprocessor 408 analyzes theelectrocardiographic signal digitized by the microprocessor 408 and theoutput signal received from the QRS detector 416 and classifies thedigitized electrocardiographic signal as either atrial fibrillation orother cardiac rhythms using an algorithm 1000, described below. The QRSdetector 416 reduces the computational load on the microprocessor 408 bydetecting the QRS complex signal and notifying the microprocessor 408 bythe output signal from the QRS detector 416.

If the digitized electrocardiographic signal is classified as atrialfibrillation, then the digitized electrocardiographic signal is retainedas digital electrocardiographic data in the data storage component 410.If the digitized electrocardiographic signal is classified as a cardiacrhythm other than atrial fibrillation, then the digitizedelectrocardiographic data is not retained in the data storage component410. Thus, only the digitized electrocardiographic data representingatrial fibrillation is retained in the data storage component 410,saving memory space which would otherwise be used for storing alldigitized electrocardiographic data. For example, using a method 1100(FIG. 11A) for detecting atrial fibrillation, discussed below, and theexample given above for 90 days of continuous recording of a singlechannel EKG at 100 samples per second and 10 bits per resolution,requires about 46 MB of storage rather than 927 MB.

An alternative embodiment would store all data that is recorded, butdata classified as atrial fibrillation is marked for earlier reviewand/or transmission to the medical facility or monitoring center.

In one embodiment, the software program running on the microprocessor408 compresses the digital electrocardiographic data before storing thedigital electrocardiographic data in the data storage component 410.Such compression effectively increases the storage capacity of the datastorage component 410. In one embodiment, the monitoring circuit 400gives an indication to the user of the portable monitoring device 202(FIG. 2A) that the electrocardiographic data retained in the datastorage component 410 is ready for retrieval. In one embodiment, theindication to the user is produced when a predetermined amount of datahas been retained in the data storage component 410. In anotherembodiment, the indication to the user is produced when a predeterminedamount of time has elapsed. Those skilled in the art will appreciatethat the indication to the user may be produced based on criteria otherthan those mentioned above. The user of the portable monitoring device202 retrieves the electrocardiographic data retained in the data storagecomponent 410 by one of the methods discussed above with respect toFIGS. 3A-3D. The electrocardiographic data may be analyzed further bymore advanced methods after retrieval.

FIG. 4B is a block diagram showing another illustrative embodiment of acircuit 450 for monitoring and detection of atrial fibrillation. Thecomponents and operation of the circuit 450 are substantially similar tothe circuit 400 described above with respect to FIG. 4A, except forlacking the QRS detector 416 shown in FIG. 4A. The functions performedby the QRS detector 416 in circuit 400, are performed by amicroprocessor 458 under software control. Therefore, the softwareprogram stored in a data storage component 460 is executed by themicroprocessor 458 to cause the detection of an analogelectrocardiographic signal, including a periodic QRS complex segment.The analog electrocardiographic signal is input at input terminals 468and 470, amplified by a preamplifier 452, and transmitted to themicroprocessor 458.

As discussed above with respect to FIG. 4A, the microprocessor 458digitizes the analog electrocardiographic signal which is used by thesoftware program to detect the periodic QRS complex. The operation ofthe circuit 450 is otherwise the same as the circuit 400 discussedabove. As discussed above, it will be appreciated by those skilled inthe art that other electronic components not shown in FIG. 4B, such asLCD display, buttons, LEDs, and the like, may be coupled to the circuit450.

FIG. 5 shows a signal 500 representing a heart rhythm. The heart rhythmsignal 500 comprises a repeating pattern of several distinct segments,including an intermittent event referred to in the art as the QRScomplex 502. The QRS complex 502 comprises a peak 504. The time intervalbetween two consecutive peaks 504 is the interbeat interval, or RRinterval 506. The peak 504 is one of the QRS complex 502 features whichcan be used to detect a QRS complex 502. An instantaneous heart rate canbe defined as the inverse of the RR interval 506, that is, instantaneousheart rate equals 1/RR-interval (or 60/RR-interval, for beats perminute), for each RR interval 506.

The detection methods 1100 discussed below in more detail with respectto FIG. 11A is used to detect atrial fibrillation by analyzing anelectrocardiogram signal 500 with a high degree of accuracy. Adiscrimination method 1180 discussed below with respect to FIG. 11Bshares some of the steps of the detection method 1100, but is directedto discriminating a detected pattern to determine if an arrhythmia is ofa type where intervention is indicated. QRS detection methods 1200 aredisclosed and discussed with reference to FIG. 12. The disclosedarrhythmia detection and discrimination methods 1100, 1180 are generallybased on analyzing the variability of the RR interval 506 to detect anddiscriminate characteristic of fibrillation. Detection method 1100 usesthe actual time of occurrence of the QRS complexes 502 to detect atrialfibrillation.

FIG. 6 is a pictorial diagram of a heart signal fragment 600 includingthe QRS complex 602 of a representative heart rhythm. The QRS complex602 comprises a wave valley section Q, a peak section R, and anothervalley section S. As discussed above, the QRS complex appearsperiodically in a heart rhythm. In the present method, the time ofappearance of each QRS complex 602 is used to detect the variability ofthe heart rhythm.

FIG. 7 is a pictorial diagram comparing illustrative box plots 700 ofvalues obtained for atrial fibrillation 704 and normal heart rhythm 724,using data from the atrial fibrillation and normal sinus rhythmdatabases of PhysioNet. Goldberger, A. L., et al., PhysioBank,PhysioToolkit, and PhysioNet: Components of a New Research Resource forComplex Physiologic Signals, Circulation 101(23):e215-e220 2000 (June13). The box plots 704 and 724 are constructed based on the calculationsdescribed in FIG. 11A, which represent a measure of the local deviationof the heart rate. The local deviation is closely related to variance,indicating the variability of heart rate. Bassingthwaighte, J. B. andRaymond, G. M., Evaluation of the Dispersional Analysis Method forFractal Time Series, 23(4) Ann. Biomed. Eng. 491 (1995). The verticalaxis of box plots 700 is the median of median values for heart ratedeviation.

The box plot 704 for a typical atrial fibrillation pattern shows a meanvalue 708, an upper edge 710 indicating the 75th percentile locatedabove the mean 708, a lower edge 712 indicating the 25th percentilelocated below the mean 708, an upper line 702 indicating 1.5interquartile (interquartile range is a measure of spread or dispersionand is the difference between the 75th percentile and the 25thpercentile) above the mean 708, and a lower line 706 indicating 1.5interquartile below the mean 708.

In contrast, the box plot 724 for a normal heart rhythm, comprising amean value 728, an upper edge 730 indicating 75th percentile locatedabove the mean 728, a lower edge 732 indicating 25th percentile locatedbelow the mean 708, an upper line 722 indicating 1.5 interquartile abovethe mean 728, and a lower line 726 indicating 1.5 interquartile belowthe mean 728, is much more compact.

Based on the variability of heart rate indicated by box plots 704 and724, significant discrimination between atrial fibrillation and normalsinus rhythm exists, which discrimination is detectable by the method1100 (FIG. 11A), discussed below, using a median of median values forheart rate deviation. A median of a number of statistical samples issignificant because the median is a non-linear average valuerepresenting the statistical samples and is defined as the middle valueof a sorted list of the statistical samples. It is non-linear withrespect to the values of the statistical samples because the value ofthe median does not change with value of each statistical sample, incontrast to a mean value of the same samples. (The mean value changeslinearly with the changes in the values of the statistical samplesbecause the mean is equal to the sum of the values of all thestatistical samples divided by the number of the statistical samples.)Thus, the median value is not sensitive to and does not change as aresult of changes in sample values at the extreme ends of a statisticalpopulation.

FIG. 8 is an illustrative plot 800 of values for sensitivity versus[1—specificity] (one minus specificity) with a variable threshold fordetection of atrial fibrillation. The plot 800 comprises a receiveroperator curve (“ROC”) 806. This ROC 806 relates the sensitivity value804 versus one minus specificity (1—specificity) value 802, calculatedbased on different threshold values for the present embodimentcomprising a median of median values for heart rate deviation. A medianof median values for heart rate deviation, discussed above with respectto FIG. 7, above a given threshold value is considered to indicateatrial fibrillation subject to error rates defined by sensitivity andspecificity values. Clearly, if the selected threshold is too low, thelow sensitivity for detecting AF will be very good (near or equal to1.0) but false positives will be high, resulting in poor specificity.Conversely, if the threshold is too high, false positives will bereduced, but actual AF events will be missed (false negatives).

Errors in detection of atrial fibrillation are classified as falsepositives (“FP”) and false negatives (“FN”), as discussed above in thebackground section. Threshold values are selected such that sensitivityand specificity values are maximized. The bend in the ROC 806corresponds to a threshold that results in minimal error, that is,maximal sensitivity and specificity.

FIG. 9 is an illustrative plot 900 of a number of RR intervals 902 usedto detect AF versus area under the ROC 904. The plot 900 comprises anarea under ROC curve 906 obtained by plotting the area under each ROC806 (e.g., FIG. 8) versus number of RR intervals 902 used in calculatingthe median of median values for heart rate deviation. Sensitivity andspecificity increase when the number of RR intervals 902 increases,resulting in a larger area under the ROC 904, which has a maximum valueof 1. The bend in the area under ROC curve 906 corresponds to the numberof RR intervals 902 at which the area under ROC curve 906 is nearmaximum, and increasing the number of RR intervals 902 has less effectto increase the performance of the method, as measured by the area underROC curve 904 value. Therefore, the use of a larger number of RRintervals 902 only marginally increases the area under ROC curve 906while increasing the computational load on a portable monitoring device202. Therefore, by plotting the area under ROC curve 906, a near optimalnumber of RR intervals 902 may be obtained, minimizing the number of RRintervals 902 to be used in calculations while maximizing thesensitivity and specificity defined by the corresponding ROC 806.

For example, if 19 RR intervals 902 are used for computation, athreshold may be chosen that provides a sensitivity value of 98.0% andspecificity value of 98.7%. The above-mentioned sensitivity andspecificity values are close to those resulting from using 7 RRintervals 902 (98.0% and 97.2%, respectively), but the cost ofcomputation and storage with 19 RR intervals 902 is greater than with 7RR intervals 902.

Persons of skill in the art will understand that the desired sensitivityand specificity may vary depending on a number of factors, including forexample a particular patient's medical and physical condition, thenature of the particular application, etc. In an application whereinmonitoring is conducted in conjunction with an in situ interventionmechanism such as an implantable defibrillator, for example (see FIG.11B, and the associated disclosure), the importance of intervention inany potentially life-threatening situation may outweigh the risks anddiscomfort associated with the intervention. Therefore, the thresholdmay be chosen to achieve a very high sensitivity, with less concernabout specificity.

FIG. 10 is a pictorial diagram showing an illustrative set of threesegments of RR intervals 1008. For the arrhythmia detection algorithmthe three segments used include segment J−1 1002, segment J 1004, andsegment J+1 1006. Each of the segments J−1 1002, J 1004, and J+1 1006comprises an equal number of RR intervals 902. Each RR interval ismeasured based on the arrival time of each QRS complex 502, as shown inFIG. 5. The above-mentioned segments are used in method 1100 discussedbelow.

For the preferred arrhythmia discrimination method 1180 (see, FIG. 11B)disclosed herein, only a single segment, for example, segment J 1004, isused for computation. As discussed above, the segment J 1004 comprises apredetermined number of RR intervals 1008. Although the illustrativesegments shown in FIG. 10 comprise seven heart beat intervals, asdiscussed above different numbers of heart beat intervals may beselected to comprise an analysis segment. For example, in a preferredembodiment, the analysis segments comprise between three and nineteenheart beat intervals.

FIG. 11A is a flow diagram of an illustrative atrial fibrillationdetection method 1100. The method 1100 measures the variability of heartrate using the instantaneous heart rate based on actual arrival times ofQRS signals 502. The method 1100 further compares a non-linear valuerepresenting the variability of heart rate to a selected threshold. Themethod 1100 determines the existence of atrial fibrillation based on theresult of the comparison. The flow diagram proceeds to block 1110 wherethe duration of each RR interval 1008 is determined within each of anumber of segments, for example, the three segments J−1 1002, J 1004,and J+1 1006 shown in FIG. 10. It will be appreciated by those skilledin the art that any number of segments may be used for this calculationand the choice of three segments is for the purpose of illustration onlyand is not to be construed as a limitation on the invention. The flowdiagram proceeds to block 1120 where instantaneous heart ratescorresponding to each RR interval 1108 are determined within each of thesegments J−1 1002, J 1004, and J+1 1006 separately.

In block 1122 the mean value of the instantaneous heart rates arecalculated for each segment. In block 1125 the mean values of each ofthe segments J−1 1002, J 1004, and J+1 1006 are subtracted. In block1130 the linear trend is removed to improve estimation of variability ofinstantaneous heart rate. In block 1140 the absolute value of thedeviation from mean with linear trend removed is determined for each RRinterval 1108 within each of the segments J−1 1002, J 1004, and J+11006. In block 1150 the median of absolute deviation from mean for eachRR interval 1108 is determined. In block 1160 the median of medians ofall three segments J−1 1002, J 1004, and J+1 1006 is obtained. In block1165 the median of medians of all three segments J−1 1002, J 1004, andJ+1 1006 is compared with a chosen threshold value. If the median ofmedians of all three segments J−1 1002, J 1004, and J+1 1006 is greaterthan the chosen threshold, then existence of atrial fibrillation isprobable 1170 in segment J 1004. Otherwise, atrial fibrillation isunlikely to exist 1175 in segment J 1004.

FIG. 11B is a flow diagram of an illustrative life-threateningarrhythmia discrimination method 1180 and is similar to the flow diagramof FIG. 11A in relevant portions. As such, only the differences betweenthe two flow diagrams are described below with respect to FIG. 11B. Asnoted above, the life-threatening arrhythmia discrimination method 1180uses a single segment of data J 1004 (FIG. 10), rather than multiplesegments discussed with respect to FIG. 11A. Blocks 1180-1192 of FIG.11B generally follow the same procedures described with respect toblocks 1100-1150 in FIG. 11A. A number N of RR intervals 1008 thatcomprise a segment is selected 1182, and the duration of each RRinterval 1108 is used to determine an instantaneous heart rate 1184 foreach interval. The mean (“M”) of the instantaneous heart rate values forthe segment is calculated 1185. The mean is subtracted from theinstantaneous heart rate values 1186, and the linear trend is removed1188. The absolute deviation (the absolute value of the result from1188) is calculated 1190 for each RR interval 1108 in the segment, andthe absolute deviation median value (“V”) is identified 1192.

At block 1193 a result is calculated that is used to determine if anelectric shock or other intervention is indicated. It has been foundthat very good specificity and sensitivity for determining ifintervention is appropriate can be achieved using a result comprising alinear combination of the instantaneous heart rate mean, M, and themedian of the absolute deviation, V. In particular, a linear combinationof M and V can be used to obtain a result that is then compared with adesired threshold, wherein if the result exceeds the threshold thenintervention is indicated. This linear combination may be representedas: (A*M)+(B*V), where A and B are empirically obtained constantcoefficient values.

In the current embodiment the empirically determined coefficients, A andB, have values in the ranges of 1.70 to 2.00 and −0.50 to −0.60respectively. The coefficients A and B have preferred values of 1.875and −0.53125, respectively. At block 1194 the result obtained at block1193 is compared with a desired threshold. If the threshold is exceeded,the method proceeds to block 1196 where the method indicates that thetype of arrhythmia detected requires electric shock. Otherwise, themethod indicates that intervention is not indicated 1198.

Of course, it is possible to use data from the atrial fibrillation,normal sinus rhythm and ventricular tachyarrhythmia databases, such asthe data from PhysioNet™ referenced above, to determine a suitablethreshold for use in the present application, using the procedureoutlined with reference to FIGS. 8 and 9.

FIG. 12 is a flow diagram of an illustrative QRS detection method 1200.As discussed above with respect to FIG. 4B, method 1200 may be used by asoftware program executed by the microprocessor 458 to detect a QRScomplex 602. The method 1200 detects the QRS complex 602 by summing upthe absolute value of the amplitudes of the signal samples that fallwithin a QRS complex 602 time window and comparing the sum with athreshold. More specifically, in block 1202, an IN_QRS flag is reset. Inblock 1205, the next signal sample is obtained for detection of the QRScomplex 602. In block 1210, an amplitude difference between the nextsignal sample and the immediate previous signal sample is calculated. Inblock 1215, an absolute value of the amplitude difference between thenext and the immediate previous signal samples is calculated. In block1220, the total of the absolute values of amplitude differences over apredetermined time window is calculated. In block 1225, the total of theabsolute values of amplitude differences is compared with a threshold.If the total of the absolute values of amplitude differences is greaterthan the threshold, the flow diagram proceeds to block 1230 where it isdetermined whether the time of the next signal sample is within the timewindow of the QRS complex 602. Otherwise, the flow diagram proceeds toblock 1250. Back in block 1230, if the time of the next signal sample iswithin the QRS complex 602, the flow diagram proceeds to block 1235where it is determined whether the total of the absolute values ofamplitude differences of the next signal sample is greater than acurrent maximum QRS amplitude value, MAX_QRS. If the total of theabsolute values of amplitude differences is greater than MAX_QRS, theflow diagram proceeds to block 1240 where the time of the next signalsample is marked as a fiducial point. The flow diagram proceeds to block1245 where the value of the maximum QRS is updated and set to the totalof the absolute values of amplitude differences. The flow diagramproceeds to block 1265 where it is determined if more sample signals areavailable. If more sample signals are available, the flow diagramproceeds back to step 1205 to get the next signal sample. Otherwise, theflow diagram terminates at block 1270.

Back in block 1225, if the total of the absolute values of amplitudedifferences is not greater than the variation threshold, the flowdiagram proceeds to block 1227 where the state of the IN_QRS flag isdetermined. If the IN_QRS flag is set, the flow diagram proceeds toblock 1250. Otherwise, the flow diagram continues to block 1265. Inblock 1250, the flow determines whether the time distance from thefiducial mark is greater than a maximum QRS time value, MAX_QRSTIME.MAX_QRSTIME indicates the maximum time span that a QRS complex 602 mayhave. If the time distance from the fiducial mark is greater than theMAX_QRSTIME, the flow diagram proceeds to block 1255 where the MAX_QRSvalue and IN_QRS flag are reset. The flow diagram proceeds to block1260, where the difference between the fiducial time and half thepredetermined time window is provided by the method 1200 as the time ofthe QRS signal. Back in block 1230, if the time of the next signal isnot within the QRS complex 602, the flow diagram proceeds to block 1232where an IN_QRS flag is set to indicate the start of a new QRS complex602, and a value is set for the maximum QRS. The flow diagram proceedsto block 1265 and continues as discussed above.

The methods and systems described above allow identification of patientsat risk due to otherwise undetected atrial fibrillation. For example,studies may be performed to assess the risk as a function of the amountand duration of atrial fibrillation, in the patients known to haveparoxysmal atrial fibrillation. These methods and systems could alsoallow automated or semi-automated treatment of atrial fibrillation,either with medications or electrical shocks (cardioversion).

The methods and systems also allow the prevention of inappropriatedefibrillation (shocks) in individuals with an implantable cardiacdefibrillator, while still allowing life-saving shocks.

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.For example, while the methods and systems described above are directedtowards the detection of atrial fibrillation, and prevention ofinappropriate shocks, other infrequent but clinically significant rhythmdisturbances, such as ventricular tachycardia or intermittent high-gradeatrioventricular block, may be detected by substantially similar methodsand systems.

The embodiments of the invention in which an exclusive property orprivilege is claimed are define as follows:
 1. A wearable cardiacarrhythmia monitoring and detection device comprising: a substratehaving a first end incorporating a first electrode, a second endincorporating a second electrode, and an intermediate portion connectingthe first and second ends, the intermediate portion incorporating anintegrated monitoring device; wherein the integrated monitoring devicecomprises: (i) an amplifier configured to receive and amplifyelectrocardiography data from the first and second electrodes; (ii) aprogrammable microprocessor configured to receive theelectrocardiography data from the amplifier and to analyze the receiveddata; (iii) a data storage device configured to receive and store atleast a portion of the electrocardiography data from the programmablemicroprocessor; and (iv) a power supply; and further wherein theintegrated monitoring device is configured to: (v) determine theduration of heart beat intervals from the electrocardiography data for aselected analysis segment comprising a plurality of heart beatintervals; (vi) calculate an instantaneous heart rate for each of theheart beat intervals in the analysis segment; (vii) calculate a testresult using the calculated instantaneous heart rates; and (viii)compare the test result with a predetermined threshold to discriminatebetween different types of cardiac arrhythmia.
 2. The wearable cardiacarrhythmia monitoring and detection device of claim 1, wherein theprogrammable microprocessor is configured to identify cardiacarrhythmia, and further wherein only electrocardiography data associatedwith detected cardiac arrhythmia is stored by the data storage device.3. The wearable cardiac arrhythmia monitoring and detection device ofclaim 2, wherein the microprocessor compresses the electrocardiographydata associated with detected atrial fibrillation prior to the databeing stored by the data storage device.
 4. The wearable cardiacarrhythmia monitoring and detection device of claim 2, wherein thecardiac arrhythmia comprises atrial fibrillation.
 5. The wearablecardiac arrhythmia monitoring and detection device of claim 4, whereinthe integrated monitoring device is configured to: (i) determine theduration of heart beat intervals from the electrocardiography data for aselected analysis segment comprising a plurality of heart beatintervals; (ii) calculate an instantaneous heart rate for each of theheart beat intervals in the analysis segment; (iii) calculate a testresult using the calculated instantaneous heart rates; and (iv) comparethe test result with a predetermined threshold to identify an event ofatrial fibrillation.
 6. The wearable cardiac arrhythmia monitoring anddetection device of claim 4, wherein the integrated monitoring device isfurther configured to: (v) calculate a mean instantaneous heart rate forthe analysis segment; (vi) calculate a deviation from the meaninstantaneous heart rate for each heart beat interval in the analysissegment; and (vii) determine a median deviation from the meaninstantaneous heart rate in the analysis segment; and wherein the testresult is calculated using the mean instantaneous heart rate and themedian deviation from the mean instantaneous heart rate.
 7. The wearablecardiac arrhythmia monitoring and detection device of claim 6, whereinthe test result is calculated as a linear combination of the meaninstantaneous heart rate and the median deviation from the meaninstantaneous heart rate.
 8. The wearable cardiac arrhythmia monitoringand detection device of claim 1, wherein the analysis segment comprisesbetween five and nineteen heart beat intervals.
 9. The wearable cardiacarrhythmia monitoring and detection device of claim 1, wherein theintegrated monitoring device further comprises a wave pattern detectiondevice that is configured to receive electrocardiography data from thefirst and second electrodes.
 10. The wearable cardiac arrhythmiamonitoring and detection device of claim 9, wherein the wave patterndetection device is a QRS detector operable to detect a QRS complexsignal from the received electrocardiography data, and to provide QRSinformation to the microprocessor.
 11. The wearable cardiac arrhythmiamonitoring and detection device of claim 9, wherein the wave patterndetection is performed by the microprocessor, and functions as a QRSdetector operable to detect a QRS complex signal from the receivedelectrocardiography data, and to provide QRS information to themicroprocessor.
 12. The wearable cardiac arrhythmia monitoring anddetection device of claim 1, wherein the integrated monitoring devicefurther comprises a wireless module configured to communicate data fromthe integrated monitoring device to an external computer.
 13. A wearableatrial fibrillation monitoring and detection device comprising: asubstrate having a first end incorporating a first electrode, a secondend incorporating a second electrode, and an intermediate portion withan embedded monitoring system; wherein the embedded monitoring systemcomprises: (i) a preamplifier configured to receive electrocardiographydata from the first and second electrodes; (ii) a microprocessorconfigured to receive the electrocardiography data from the amplifierand to analyze the received data; (iii) a data storage device configuredto receive and store at least a portion of the electrocardiography datafrom the microprocessor; and (iv) a power supply; and further whereinthe embedded monitoring system is configured to: (v) determine theduration of heart beat intervals from the electrocardiography data foran analysis segment comprising a plurality of heart beat intervals; (vi)calculate an instantaneous heart rate for each of the heart beatintervals in the analysis segment; (vii) calculate a test result usingthe calculated instantaneous heart rates; and (viii) compare the testresult with a predetermined threshold to identify an atrial fibrillationevent.
 14. The wearable atrial fibrillation monitoring and detectiondevice of claim 13, wherein the microprocessor is configured to detectatrial fibrillation, and further wherein only electrocardiography dataassociated with detected atrial fibrillation is stored on the datastorage device.
 15. The wearable atrial fibrillation monitoring anddetection device of claim 14, wherein the microprocessor compresses theelectrocardiography data associated with detected atrial fibrillationprior to the data being stored on the data storage device.
 16. Thewearable atrial fibrillation monitoring and detection device of claim14, wherein the embedded monitoring system is further configured to: (v)calculate a mean instantaneous heart rate for the analysis segment; (vi)calculate a deviation from the mean instantaneous heart rate for eachheart beat interval in the analysis segment; and (vii) determine amedian deviation from the mean instantaneous heart rate in the analysissegment; and wherein the test result is calculated using the meaninstantaneous heart rate and the median deviation from the meaninstantaneous heart rate.
 17. The wearable atrial fibrillationmonitoring and detection device of claim 16, wherein the test result iscalculated as a linear combination of the mean instantaneous heart rateand the median deviation from the mean instantaneous heart rate.
 18. Thewearable atrial fibrillation monitoring and detection device of claim14, wherein the analysis segment comprises between five and nineteenheart beat intervals.
 19. The wearable atrial fibrillation monitoringand detection device of claim 13, wherein the embedded monitoring systemfurther comprises a wave pattern detection device that is configured toreceive electrocardiography data from the first and second electrodes.20. The wearable atrial fibrillation monitoring and detection device ofclaim 19, wherein the wave pattern detection device is a QRS detectoroperable to detect a QRS complex signal from the receivedelectrocardiography data, and to provide QRS information to themicroprocessor.
 21. The wearable atrial fibrillation monitoring anddetection device of claim 19, wherein the wave pattern detection isperformed by the microprocessor, and comprises a QRS detector operableto detect a QRS complex signal from the received electrocardiographydata, and to provide QRS information to the microprocessor.
 22. Thewearable atrial fibrillation monitoring and detection device of claim13, wherein the embedded monitoring system further comprises a wirelessmodule configured to communicate data from the integrated monitoringdevice to an external computer.